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Chapter 2

Radio Resource Management Performance

for the GSM/EDGE Radio Access Network

Yuri C. B. Silva, Tarcisio F. Maciel, and Francisco R. P. Cavalcanti

2.1 Introduction

This chapter presents a broad study on the potential of applying RRM techniques to

the global system for mobile communication (GSM)/enhanced data rates for GSM

evolution (EDGE) system. Even though the presented results are focused on the

GSM/EDGE system, the principles employed by most of the considered RRM tech-

niques can be applied to other radio access networks (RANs). We thus hope that the

reader will also learn about RRM strategies and adapt these concepts to the RAN ofhis/her own interest.

The provision of multiple services is one of the key features of GSM/EDGE,

which has been the focus of several studies, such as [22, 24, 25, 37]. Additionally,

some of the themes that have been covered by recent research include power con-

trol [36, 41, 45], dynamic channel allocation [20, 46, 47, 49, 60], multi-antenna

techniques [18, 19, 34, 48, 50, 51], among others.

In this chapter, the RRM techniques are placed within the context of GSM/EDGE

and results are presented, which indicate the achievable gains in terms of capacity

and/or quality of service (QoS). The results demonstrate that, by using appropriateRRM techniques, the GSM/EDGE radio access network capacity remains compet-

itive with other emerging access technologies, thus allowing for substantial opera-

tional cost reductions for the already deployed infrastructure.

The remaining of this chapter is organized as follows: Section 2.2 briefly de-

scribes the architecture of the GSM/EDGE radio access network, along with its

protocol stack and standard RRM functionalities. Section 2.3 presents the RRM

techniques considered in the scope of this chapter, which are power control, dy-

namic channel allocation, management of multiple services, and multi-antenna tech-

niques. Next, in Section 2.4, some aspects concerning the simulation and modelingof GSM/EDGE networks are discussed. The achieved simulation results are pre-

sented in Section 2.5 for the studied RRM techniques. Finally, trends and direc-

tions for the further evolution of RRM in GSM/EDGE networks are discussed in

Section 2.6.

F. Cavalcanti, S. Andersson (eds.), Optimizing Wireless Communication Systems, 51

DOI 10 1007/978 1 4419 0155 2 2 S i S i B i M di LLC 2009


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52 Y. C. B. Silva, T. F. Maciel, and F. R. P. Cavalcanti

2.2 Fundamentals of RRM in GSM/EDGE

The second generation of cellular systems was marked by a transition from analog-

to-digital radio communications. GSM emerged in this context, with its phase 1

specification and initial deployment dating back to the early 1990s. GSM had a sig-nificant role in unifying the previously diverging European standards. The ubiquity

of GSM, which facilitated international roaming among operators, the creation of

the low-cost short message service, the support for circuit-switched data connec-

tions, as well as further improvements of the technology, such as the introduction

of more efficient speech codecs, led to widespread GSM availability throughout the

world, reaching the expressive mark of over 3 billion subscribers by the end of 2007

[27].

The provision of data services was improved with the introduction of the general

packet radio service (GPRS) in 1997, which added support for packet-switched con-nections and provided four different coding schemes, with rates ranging from 8 to

20 kbit/s. In 1999, the enhanced GPRS (EGPRS) was introduced and then adopted

as the packet system of the GSM/EDGE radio access network, which is the focus

of this section. In the following section, an overview of GSM/EDGE is presented

along with some of its standard functionalities.

2.2.1 GSM/EDGE Radio Access Network Overview

The GSM/EDGE radio access network (GERAN) represents the evolution of the

GSM system for providing improved packet data transmission. The GPRS and

EGPRS are radio technologies that provide packet-switched connections between

MS and BS, while the GERAN is composed of several network elements that are

interconnected through standard interfaces. The two main elements of the radio ac-

cess network are

Base station subsystem (BSS): It comprehends the base transceiver station

(BTS), or simply BS, which is the onsite base station, and the base stationcontroller (BSC), which is a controlling unit responsible for a group of BTSs.

Core network: It has functionalities such as mobility management, authentica-tion, charging, among others, and also provides access to networks outside of the

cellular system. In the case of circuit-switched connections, the mobile switching

center (MSC) is the main element of the core network, providing accessibility

to the conventional public-switched telephone network (PSTN). In the case of

packet-switched connections, the main elements are the service GPRS support

node (SGSN) and the gateway GPRS support node (GGSN). The former per-

forms routing and delivery of packets within the cellular system and the latterprovides connectivity to external data packet networks, such as the Internet.

With the purpose of maintaining compatibility with the existing GSM infrastruc-

ture, the EDGE technology has many parameters in common with GSM, including


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2 RRM Performance for GSM/EDGE Radio Access Network 53

the sharing of the same frequency spectrum. EGPRS introduces some improvements

with regard to GPRS, such as the 8-PSK (phase-shift keying) modulation and ad-

ditional modulation and coding schemes (MCSs). Due to this improved physical

layer, EGPRS provides high data rates, reaching 384 kbit/s or more when multiple

timeslots are reserved to a single MS.Through the adequate configuration of parameters from the protocol layers of

GSM/EDGE, it is possible to provide multiple services. The data transmission of

EGPRS may be adjusted, for example, to support applications with different quality-

of-service (QoS) requirements, such as World Wide Web (WWW), file transfer

protocol (FTP), and streaming of audio/video files.

The standardization of the GSM/EDGE radio access network is coordinated by

the 3rd. Generation Partnership Project (3GPP). The standards have undergone some

major revisions, with Rel-8 being the latest release as of 2008.

The GSM/EDGE network is already well established, in terms of technical matu-rity as well as market deployment. This, however, has not stopped the development

of new techniques for improving its performance and providing capacity gains.

2.2.1.1 Channel Structure

The GSM/EDGE system implements multiple access through frequency division as

well as through time division. Each frequency carrier has a cyclic time structure

associated, which is composed of hyperframes, superframes, multiframes, frames,

and timeslots [7], as it can be seen in Fig. 2.1.

Fig. 2.1 GSM/EDGE frame

structure.

Hyperframe = 2048 superframes

Superframe = 51 multiframes

Multiframes = 26 frames (120 ms)

Frame = 8 timeslots

0

0

0

0 1

1

1 2046 2047 24 25

749 50TSTS

Besides the displayed frame structure, which is employed for traffic channels,

there is an alternative signaling frame structure that defines a multiframe with 51

frames and a superframe with 26 multiframes.2.1

The basic time unit is the timeslot, which is equivalent to roughly 0.577 ms. A

sequence of eight timeslots defines a time division multiple access (TDMA) frame,

and a group of four frames composes a TDMA radio block.

Among the 26 frames of the multiframe structure in Fig. 2.1, the 13th and the

last frame are reserved for control and other functionalities. Therefore, the other 24

frames may be employed for traffic, i.e., six radio blocks.

A physical channel is defined by the pair timeslot/frequency. A logical channel,

on the other hand, corresponds to the information flow between a BS and an MS.

2.1 A superframe still contains 26 51 frames in total.
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The logical channels may be divided into traffic and control channels. Next, some

of the most relevant logical channels are presented:

Traffic channel (TCH): This is a circuit-switched traffic channel used for voiceas well as circuit-switched data transmission.

Packet data traffic channel (PDTCH): This is a packet-switched traffic channelused for data transmission.

Broadcast control channel (BCCH): This is a downlink control channel thatdistributes general information to the MSs concerning the system configura-

tion. The information may include number of common control channels, possi-

ble combinations of control channels, whether support for packet-switched traf-

fic is enabled, among others. There is also the packet broadcast control channel

(PBCCH), which is the corresponding channel for data MSs.

Common control channel (CCCH): It corresponds to a set of common control

channels that are used for implementing access management functions. Dedicated control channel (DCCH): It corresponds to a set of dedicated con-

trol channels that are used for measurements, signaling, among other func-

tionalities. The main circuit-switched dedicated control type channels are the

slow associated control channel (SACCH) and fast associated control channel

(FACCH), which provide connection-specific signaling information concerning

the channels they are associated to, and the stand-alone dedicated control channel

(SDCCH), which can be used for signaling during call setup. These channels can

be used both in the uplink and downlink.

The SACCH, for example, is important for transmitting information related to

signal level and signal quality measurements. The reporting periods have a duration

of 480 ms (104 TDMA frames) and they are employed, e.g., by the power control

(PC), handover, and link adaptation (LA) algorithms discussed later in this chapter.

The actual radio transmission requires that the different logical channels be

mapped onto the physical channels. A physical channel may be composed of only

control channels, two half-rate traffic channels plus control channels, or a full-rate

traffic channel plus control channels. The combination of traffic and control chan-

nels is possible by either employing the previously described reserved frames, in the

case of SACCH, or by stealing slots from traffic channels, in the case of FACCH. A

more detailed description of the possible channel combinations can be found in [6].

2.2.1.2 Protocols

This section presents an overview of the protocol stack of the GSM/EDGE network

for packet data transmission. Figure 2.2 shows how the user plane2.2 protocol layers

are organized among the different network elements in the 3GPP standards.

The focus of this chapter lies on the radio link between the MS and the BS, whichis supported by the following three protocol layers:

2.2 There are protocol stacks for the user plane and control plane. The former refers to the actual

data transmission and the latter is used for control and signaling.
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SNDCP

RLC

MAC

GSM RF

RLC

MAC

GSM RF L1bis L1bis

SNDCP

MS NSGSSSB

LLC

BSSGP

Network

service

LLC

BSSGP

Network

service

GTP-U

L1

L2

UDP

IP

Relay

GTP-U

L1

L2

UDP

IP

IP IP

GGSN

Application

Relay

Fig. 2.2 Packet-switched user plane protocols of the GSM/EDGE network.

Link layer control (LLC): It offers a reliable and secure logical link betweenthe MS and the SGSN for superior layers. One of its main functionalities consists

of performing the segmentation of packets arriving from higher layers. Radio link control (RLC) and medium access control (MAC): These pro-tocols provide services for the transfer of information over the physical layer.

Among their functionalities are the error-correcting procedures enabled through

the selective retransmission of erroneous blocks. The RLC function offers a re-

liable radio link to the higher layers, while MAC treats issues such as channel

allocation and the multiplexing/scheduling of MSs.

GSM RF or physical layer: It provides data transfer services over the physi-cal channel between the BS and the MS. Among its functionalities are the cod-

ing of data and the detection/correction of transmission errors in the physicalmedium.

The data transmission process can be briefly described as follows. The packets

that arrive from the internet protocol (IP) and sub-network-dependent convergence

protocol (SNDCP) layers are segmented into LLC layer frames. The LLC frames

are segmented into RLC/MAC blocks as they are being requested by the system.

The RLC/MAC blocks are transmitted over the four bursts of a TDMA radio block,

where the term burst corresponds to the transmission of data during the time of a

timeslot.

The RLC/MAC blocks are composed of header and data fields, which have vari-able lengths depending on the current MCS. EGPRS has nine MCSs, with the first

four employing Gaussian minimum shift keying (GMSK) modulation and the re-

maining ones 8-PSK. The lowest MCSs transport a smaller amount of information

data per block, but are more robust to variations in the link quality.

The MCSs are organized into different families, each with a certain base pay-

load [4]. In the case of retransmissions, only an MCS of the same family may

be chosen. MCSs 79 transmit two RLC/MAC blocks per TDMA radio block,

while the others transmit only one block. A summary of the main MCS param-

eters is presented in Table 2.1. The interested reader can refer to [3, 5] in or-der to obtain a detailed description of each protocol in the GSM/EDGE protocol

stack.


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Table 2.1 MCS parameters (with data rate in kbit/s and payload in bytes per TDMA radio block).

MCS 1 2 3 4 5 6 7 8 9

Modulation GMSK 8-PSK Code rate 0.53 0.66 0.85 1.0 0.37 0.49 0.76 0.92 1.0

Data rate 8.8 11.2 14.8 17.6 22.4 29.6 44.8 54.4 59.2

Payload 22 28 37 44 56 74 112 136 148

Family C B A C B A B A A

2.2.2 Link Adaptation

The link adaptation (LA) mechanism of EGPRS, which is described in [8], tries toprovide the best possible quality to the MS through the modification of the current

MCS. This adaptation occurs according to the availability of link quality measure-

ments and it intends to exploit the channel diversity and maximize data rates by

suitably selecting an MCS according to the channel state.

Ideally, LA could be employed on a per-block basis, i.e., a new MCS would be

selected for each radio block (20 ms) [38, 39]. In practice, however, the standard LA

mechanism uses the same link quality estimations used by power control, which are

periodically reported to the BS each 480 ms [9].

Figure 2.3, based on results presented in [22], shows how LA behaves accordingto the SIR. For a given SIR, the MCS providing the highest data rate should be

selected.

Fig. 2.3 Link adaptation with

pedestrian mobility (3 km/h).

0 5 10 15 20 25 30 350

10

20

30

40

50

60

MCS 1 9

MCS 9 + IR

1234

5

6

7

8

9

SIR in dB

Throughpu

tinkbit/s

Another mechanism, which may replace or act together with LA, is the incre-

mental redundancy (IR), which is also shown in Fig. 2.3. With IR the amount of

redundancy is increased for each additionally required retransmission. IR improves


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the reception of retransmissions by combining the retransmitted blocks with the data

already available at the receiver. More details on IR can be found in [23, 40].

2.2.3 Frequency Hopping

The frequency hopping (FH) technique consists of periodically changing the trans-

mission frequency with the purpose of introducing diversity. The diversity effect

may include both frequency and interference diversity, which are illustrated in

Fig. 2.4 for two co-channel MSs.

Freq. 1

Freq. 2

Freq. N

...

Good fading

Deep fading

MS 1MS 2

Interference

No Frequency

Hopping

Frequency

Hopping

MS1

MS

2

MS 1

MS 2

Freq. 1

Freq. 2

Freq. N

...

Time

Fig. 2.4 Frequency hopping.

Because a different frequency is used after each hop, the MSs perceive different

fading gains at each time, as indicated by the arrows in Fig. 2.4. Consequently, the

users links do not remain for a long time in a deep fading and a more reliable

communication might be achieved.

Regarding interference, suppose some MSs perceive strong interference while

other MSs perceive weak interference (or no interference at all), as illustrated in

Fig. 2.4. Without frequency hopping, the links of some users would be subject to

high interference for a long time. By hopping across different frequencies, the set of

co-channel interferers seen by a user changes after each hop and the MSs would ex-

perience periodically changing interference profiles, so that almost the same average

interference would be perceived by all MSs.

Thus, the main benefit of frequency hopping is to average out the fading and

interference effects, thus allowing the use of more aggressive reuse patterns, suchas 1/3 and 1/1.2.3 The types of frequency hopping are described as follows:

2.3 This notation indicates the cluster size of the frequency reuse pattern, i.e., the group of

cells/sectors within which there can be no reuse of the available frequencies.
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Random frequency hopping (RFH): It performs the hopping in an unorderedfashion, according to a pseudo-random sequence determined based on system

parameters and the algorithm presented in [6].

Cyclic frequency hopping (CFH): It performs the hopping in an ordered fash-ion, according to a previously established cyclic sequence.RFH provides both frequency and interference diversity, while CFH presents fre-

quency diversity, but not interference diversity, since the co-channel MSs hop over

the same frequencies. The main system parameters involved with the frequency hop-

ping algorithms are presented in Table 2.2.

Table 2.2 Description of the frequency hopping parameters.

Parameter Description

MAL Mobile allocation list containing the frequencies available for allocation

MAIO Mobile allocation index offset indicating the offset within the MAL

Nfreq Number of frequencies per mobile allocation list

FN TDMA frame number currently in use

HSN Hopping sequence number allocated to each sector

MAI Mobile allocation index referencing the frequency of MAL to be used

The frequency hopping algorithm proposed in [6] can be described asMAI = (FN + MAIO) mod(Nfreq), if HSN = 0,MAI = (S + MAIO) mod(Nfreq), if HSN = 0, (2.1)

where the first equation relates to CFH and the second to RFH. The S variable

corresponds to the generation of the pseudo-random sequence, which is a func-

tion of the FN, HSN, N and the hopping table defined in [6]. The standard defines

64 possible orthogonal hopping sequences. The orthogonality is assured for MSs

that have the same HSN but different MAIOs, i.e., they never become co-channel

interferers.The MAIO allocation to MSs entering the system can be done either at random,

in the case of RFH, or it can follow a certain allocation algorithm, such as the one

defined in Section 2.3.2, in the case of CFH. More details on the implementation of

frequency hopping for GSM/EDGE can be found in [6, 42].

2.3 Advanced Radio Resource Management for GSM/EDGE

Since the deployment of the first GSM-based networks, the demand for mobile com-munication has increased enormously. Conventional GSM/EDGE networks have

reached their capacity limits and, in order to serve the growing demand for mobile

communication, solutions to increase system capacity are required.


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Spectrum is a scarce resource whose use is granted and regulated by estate in-

stitutions, such that a capacity expansion through the acquisition of new frequency

bands may become very expensive. In this context, techniques to optimize the usage

of radio resources, i.e., RRM techniques, gain importance as alternative to enhance

the system capacity without needing additional spectrum.This section describes several advanced RRM techniques contextualized in a

GSM/EDGE network. The RRM techniques discussed here are not necessarily an

integral part of the GSM/EDGE standard, but might be incorporated as part of pro-

prietary solutions.

2.3.1 Power Control

Power control (PC) is a well-established RRM technique which aims at, mainly,

reducing interference levels in a wireless network and conserving battery power of

terminals. A detailed discussion about PC has already been provided in Chapter 1

of this book. In this section, some particular aspects of PC in the context of

GSM/EDGE networks are detailed.

PC is well described in the standard for the uplink of GSM/EDGE networks

[9, 10]. In the downlink, PC is not a mandatory feature. However, a downlink PC

algorithm can also be implemented at the BSs as long as the restrictions imposed

by the standard are respected. The power characteristics of base and mobile stations

are described in [9, 10] and among the standard restrictions are, for example, the

usage of discrete power levels in steps of 2 dB and dynamic power ranges limited to

30 dB and to 10 dB for voice and data services, respectively.

The standard PC algorithm for the uplink in the GSM/EDGE network is a

variable-step up-down power control (UDPC) algorithm with some allowed step

sizes (all in integer multiples of 2 dB) [9, 10]. However, arbitrarily large power ad-

justments, such as 30 dB at once, are not foreseen. The up-down algorithm described

in Chapter 1 is the PC algorithm that can more easily be related to the standard al-

gorithm considered in the uplink of the GSM/EDGE network. In this chapter, the

up-down algorithm of Chapter 1 is referred to just as UDPC.

In order to apply PC, link measurements are required. Indeed, the actuation fre-

quency and performance of PC depend directly on the availability of such measure-

ments at the BS.

For the voice service, two standard measurements performed by the MS are the

received signal level (RXLEV) and the received signal quality (RXQUAL). RXLEV

values are measured in the range of110 to 48 dBm for each TDMA frame withinone SACCH multiframe and are mapped afterward to one of 64 possible levels. Av-

erage RXLEVs are then reported by the MS to its serving BS. For the RXQUAL,

the GSM/EDGE standard states that its value must be related to the BER before de-coding, also termed raw bit error rate (RBER). RBER values can be estimated, e.g.,

as part of the channel equalization or decoding processes. For example, a method to

estimate RBER values consists of comparing a reencoded version of a correctly


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decoded frame with the originally received frame, which is required due to the

data interleaving done across the half-bursts of the frame. Based on the estimated

RBER values, an average RBER over one SACCH multiframe should be computed,

mapped to one of eight possible discrete values, and reported by the MS to its serv-

ing BS, where it can be used to perform, e.g., downlink PC, LA, and handovers.Average RXLEV and RXQUAL values become available at the BS at the end of the

subsequent SACCH multiframe, thus imposing a delay of 480 ms between measure-

ment and availability of the measured values at the BS. Thus, it should be noted that

PC actuates at a very low frequency in the GSM/EDGE network with two power

adjustments each second. Some particular cases in which this frequency might be

higher are also defined by the standard.

For data services, the GSM/EDGE standard states that the link quality measure-

ments should be related to the bit error probability (BEP) within each burst of the

radio blocks received within one SACCH multiframe. The BEP of the four bursts ofa radio block are used to calculate the mean BEP (MEAN BEP) and the coefficient

of variation of the BEP (CV BEP) of the block, as described in [9]. MEAN BEP and

CV BEP values are computed for all the correctly decoded radio blocks within the

duration of one SACCH multiframe. The average of the MEAN BEP and CV BEP

values are calculated from these values, mapped to 32 and 8 values, respectively,

and reported back by the MS to its serving BS, where they can be used to perform,

e.g., downlink PC, LA, and handovers. Since data is interleaved across the bursts

of a block, the BEP of each burst should be estimated using, e.g., the same method

described for the voice service, i.e., the comparison of a reencoded version of a

correctly decoded block with the originally received one.

For data services in the GSM/EDGE network, PC is a particularly challenging

task because of the bursty nature of data traffic. The size of data packets may vary

in a broad range of values and such packets require quite different amounts of time

to be transmitted. For small packets, iterative PC may not have enough time to con-

verge to the target SINR before the packet is completely transmitted. Moreover,

depending on the adopted scheduling discipline the MS transmitting on the shared

channel may change each 20 ms, thus also affecting the convergence of PC algo-

rithms.

In the GSM/EDGE network, LA has priority over PC and the dynamic power

range available for PC is limited to 10 dB for data services instead of the 30 dB

used for the voice service. The reduced dynamic power range leads to higher average

interference levels [45].

One reason for a higher minimum transmit power is to ensure a higher reliability

in the reception of the uplink state flag (USF) transmitted within the downlink RLC

blocks. This 3-bit flag is stored in the header of RLC blocks sent on the downlink

and is used to coordinate channel accesses in the uplink. The USF is decoded by all

MSs sharing a downlink channel via TDMA and indicates which MS gets access to

the uplink channel during the next radio block. Thus, in order to enable scheduling inthe uplink, all MSs must be able to reliably decode the USF independently of their

positions within the cell. Throughout this chapter, every time the transmit power

range or the transmit antenna pattern are modified in the downlink, there is a risk


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that the USF might not be decoded correctly. This is a practical GSM/EDGE issue

that the RRM algorithms need to overcome.

PC and LA have somewhat conflicting objectives. The former usually aims at

providing just the minimum required quality to the links, for example, a target

SINR, thus reducing power consumption at MSs and the overall interference in thesystem. The latter aims at providing the highest possible data rate according to the

current link quality while employing maximum transmit power. In order to avoid

concurrence between PC and LA, previous works on PC for data services in the

GSM/EDGE network considered high target SINR values lying outside the interval

in which LA works [45]. However, with such high target SINR values, only a very

small number of links benefit from PC, while most of them transmit at full power.

EGPRS can be considered an energy-efficient service, since it maximizes through-

put for a fixed transmit power by LA and, consequently, MSs data sessions will

probably take shorter times in average. However, maximizing instantaneous ratesmight not always be the best policy, especially when considering mixed-service

scenarios where one service may be experiencing an excess quality while the other

services are below their QoS limit. This subject (co-existence of multiple services)

will be further explored in this chapter.

Different downlink PC algorithms are considered later in Section 2.5.2 of this

chapter, which involve non-standard features including power adjustments of up to

30 dB at once and higher actuation frequency, such as one power adjustment at each

120 or 20 ms. Regarding data services, the impact of increasing the dynamic power

range of PC from 10 to 30 dB is also investigated.

2.3.2 Dynamic Channel Allocation

The channel allocation procedure consists, essentially, of distributing a finite num-

ber of channels among the several base and mobile stations within a cellular net-

work. An efficient channel allocation algorithm may lead to benefits for the system,

be it in terms of reduced blocking rates or QoS improvements. Classically, algo-

rithms may be classified as fixed or dynamic [33], even though there are also thosewhich combine characteristics of both, which are called hybrid.

Dynamic channel allocation (DCA) assumes that there is a central channel pool,

from which the channels may be allocated on-demand, i.e., there is no fixed distri-

bution of the channels among the cells. This higher flexibility allows that the fluc-

tuations in the offered traffic and co-channel interference be treated with a higher

efficiency. DCA thus requires that the network be capable of offering all frequencies

within each cell, as well as providing reliable measurements of the parameters used

by the DCA algorithms (e.g., number of MSs and radio link quality).

The DCA algorithms may be classified according to the metric they optimize [59]or to their degree of centralization [15]. The first criterion considers characteristics

such as adaptability to traffic and interference, as well as channel reusability. The

second form of classification concerns the degree of centralization of the algorithm,

e.g., centralized, distributed, or locally distributed. Locally distributed algorithms


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allow the exchange of information among nearby BSs, aiming at the improvement

of the quality estimation procedure and consequently of the allocation decisions.

The centralized approach has a rather high implementation cost, mainly due to

the excessive signaling. The fully distributed strategy may also not be adequate,

since it does not provide the necessary means for reliable interference estimation. Alocally distributed algorithm represents the most feasible approach, allowing the ex-

change of information among the neighboring BSs in order to achieve more precise

interference estimates. For these reasons, and since high interference tight reuse pat-

terns provide the highest capacity potential, here we focus on a locally distributed

interference adaptive algorithm.

The application of DCA to an actual system must take into account the character-

istics and practical limitations of the cellular network. Some works have proposed

and evaluated DCA algorithms specifically for GSM/EDGE, such as the dynamic

frequency and channel allocation [46, 47].It is worth mentioning that the use of DCA in this context is not compatible

with random hopping. Due to the fact that the MSs are constantly hopping over the

frequencies in an unordered fashion, the channel selection procedure becomes irrel-

evant, since with each hop the set of co-channel interferers may change completely.

It is therefore required that frequency hopping be disabled or that a coordinated

cyclic hopping be implemented, in which the groups of co-channel interferers hop

over the same frequencies.

2.3.2.1 Measurements and SIR Estimation

The GSM/EDGE cellular network does not count with direct SIR measurements.

The SIR must be inferred based on the measurement report mechanisms available

in the network [9].

Each MS monitors the power levels arriving from nearby BSs. This information

is accumulated and periodically reported to the BS to which the MS is connected.

The measurements are done for the BCCH channel and the report period is of 104

TDMA frames (480 ms). For each frame a different BS is measured. The number

of BCCH carriers and the measurement order are parameters defined by the system.As an example, suppose that the BCCH list contains 32 elements, then there will be

(104/32) received power measurements for each carrier, i.e., three or four samples

per BS.

After the measurements within the report period are concluded, the MS has to

organize the data and prepare the report to be sent to the BS. The samples of each

carrier are averaged, and among all measured carriers only the six with the high-

est received power levels are included within the report. The transmission occurs

through the SACCH control channel during the next 480 ms. The total delay until

the report is available at the BS, including the measurement and transmission times,is therefore of roughly 1 s.

The original purpose of this measurement mechanism would be to aid in the

handover decisions, indicating which cells offer the best signal quality to the MS.

Nevertheless, it may also be employed to produce channel SIR estimates.


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The reports are based on measurements of the BCCH channel, which is always

active. In order to obtain a more realistic SIR estimation, the actual channel activity

of the BSs should be taken into account. However, this channel activity information

is not globally available, it has to be shared among the BSs [46]. In practical terms

this information exchange could be feasible, representing a signaling increase withinthe backbone of the cellular network. Note that if power control is employed in the

system, it would also be necessary to share the power adjustments of each BS, since

the BCCH measurements are done for full power.

2.3.2.2 Channel Selection and Admission Control

The considered DCA algorithm prioritizes the channel presenting the best SIR. In

the case when several channels perceive no interference, or when the estimated SIRis the same, the choice is done at random among them.

The admission control corresponds to an optional stage of the DCA algorithm.

Differently from the case with RFH, with DCA the MS will be subject to the same

interference profile for a certain period of time, therefore it is important to guarantee

that the channel is offering a minimum acceptable quality.

Even though the channel selection procedure prioritizes the channel perceiving

the best SIR, high-load situations may occur, for which even the best channel would

not be able to offer a satisfactory quality to the MS. In such cases, blocking the MS

might be a better option than letting it enter the system, since it is expected that its

QoS will probably not be satisfied. Besides, the additional interference that would

be introduced in the system is avoided.

The admission criterion can be based on a minimum SIR threshold, which

may be defined based on link-level simulation results. In the case of the en-

hanced full rate (EFR) speech codec, for example, a minimum SIR of 8 dB is

required in order to assure that the frame erasure ratio will be kept at acceptable

levels [24].

Another aspect that may be taken into account by the admission control is related

to the impact that the introduction of a new MS might have over the quality of the

MSs already allocated in the system. This impact test corresponds to performing

an estimate of how the SIR of the co-channel MSs would be degraded after admitting

a new MS. In case the admission would result in the SIR of any of the co-channel

MSs being reduced to a value below the threshold, blocking would be activated.

Note that the complexity for implementing such estimate might be prohibitive in

practical terms.

2.3.3 Management of Multiple Services

The support of multiple services, such as web-browsing, e-mail, audio/video stream-

ing, among others, is one of the main features of the third generation of cellular


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systems and beyond. Since these services have different characteristics and require-

ments, the use of efficient RRM techniques is therefore essential to ensure their QoS

levels and to optimize system capacity.

In systems with multiple services the available radio resources may be either seg-

regated or shared, i.e., different frequency groups may be reserved for the servicesor it may be allowed that they have access to the total set. The isolated approach

is not very efficient, since for situations of asymmetric load the overloaded service

does not have access to the channels reserved to the other services, even though

they may be unoccupied. The sharing of channels avoids problems of this nature,

but has the side-effect of creating a situation in which the different services cause

interference among each other, which may have certain implications on the capacity

of interference-limited systems.

Since the services have different QoS requirements and different traffic patterns,

their combination within interference-limited scenarios implies that the more de-manding service will limit system capacity, even though the other services still may

perceive sufficient QoS. In [54], a method has been proposed for balancing the QoS

in code division multiple access (CDMA) systems, based on a power allocation

methodology that allows the joint service capacity to be increased.

A more general definition for QoS balancing was presented in [22, 25], which

was called per-service capacity balancing. The referred work has demonstrated that

the system capacity is maximized when the per-service capacities are reached for

the same load. In the case of interference-limited systems, it has also been shown

that the capacity balancing may be achieved through an interference balancing pro-

cess, such as the service-based power setting (SBPS) technique for mixed-service

GSM/EDGE networks [24], which consists of applying offsets in the transmission

powers of the different services.

2.3.4 Multi-antenna Techniques

The application of multi-antenna techniques to mobile communication systems is

capable of providing capacity gains as well as the improvement of the quality of

service perceived by the subscribers. The spatial filtering realized through the use of

narrow beams, which can be either selected from a fixed set (switched fixed beams)

or adaptively steered toward the desired MSs (adaptive beamforming), is able to

significantly reduce the co-channel interference levels. This approach can be con-

sidered a more flexible and evolved form of the sectorization that is employed as a

baseline in most mobile communication systems. The interference reduction allows

for the implementation of more aggressive frequency reuse patterns, such as 1/3 or

1/1, thus resulting in higher spectral efficiencies.

The switched fixed beams, as well as adaptive beamforming based on direction-of-arrival estimation techniques, are more adequate to macrocellular environments

with low angular spread and strong line-of-sight [55]. In the case of indoor or micro-


15/45


cellular environments presenting strong multi-path components, the adaptive arrays

must rely on the estimation of the complex channel coefficients in order to adapt to

the current channel conditions [11].

The gains provided by multi-antenna techniques, however, are not limited to

those of the spatial filtering functionality. Techniques that take advantage of thespatial diversity associated with the presence of multiple antennas may also be ap-

plied, such as the maximum ratio combining (MRC) or the interference rejection

combining (IRC).

In the context of the application of adaptive antennas to multiple services in

GSM/EDGE networks there are some practical aspects that should be taken into

account, mainly with regard to the data service over EGPRS. For the voice ser-

vice there would be no problem with replacing the sector antennas with antenna

arrays, other than the restriction that they may not be applied to the BCCH car-

riers, for the same reason that other techniques such as FH and PC may not beused, which is to guarantee that all MSs have uninterrupted access to the broadcast

channel.

The results section carries out the evaluation of some multi-antenna strategies

for scenarios where both voice and data share the same frequency spectrum (see

Section 2.5.4). It assesses, among other things, the performance of a strategy for

which the adaptive antennas are applied only to the voice service, in order to avoid

the problems associated with their use within EGPRS. Since all MSs hop over the

same set of frequencies, the interference reduction for the voice MSs is also reflected

upon the data MSs, thus bringing benefits for both. The performance of the strategy

combining different antennas for the different services is also compared to the fully

multi-antenna case, i.e., with both services employing multiple antennas.

2.4 Simulation and Modeling of GSM/EDGE Networks

Studying the performance of modern wireless networks, such as GSM/EDGE, is a

complex task. Due to the large number of variables and mechanisms involved, a pure

analytical study is not feasible and computer simulations are applied to investigate

the systems characteristics of interest [31, 35].

In this chapter, the strategy of dividing the simulations into link and system

levels is employed. These two types of simulators are then connected through an

appropriate interface. This is a complexity reduction strategy, which is discussed

in more details in Chapter 7 of this book. We also employ dynamic system sim-

ulations, which include channel and traffic variabilities with time. In the follow-

ing, we describe several simulation models, which are used later to assess the per-

formance of RRM techniques in a GSM/EDGE network. In spite of considering

here a GSM/EDGE network, many of the models introduced in this section arequite general and can be used to evaluate the performance of other modern wireless

networks.


16/45


2.4.1 Cellular Grid, Frequency Reuse, and Mobility Models

In this chapter, the cellular network is modeled as a macrocellular system composed

of tri-sectored cells organized in 1/3 or 1/1 uniform frequency reuse patterns [59].

Figure 2.5 illustrates a cellular grid with a 1/3 frequency reuse.

Fig. 2.5 Uniform cellular grid

with 1/3 frequency reuse.

111

111

111

222

222

222

333

333

333

In Fig. 2.5, the sectors with same number employ the same set of channel fre-

quencies and are co-channel interferers. A number of MSs are distributed over the

area covered by the cellular grid and can freely move within it. Each cell sector in

Fig. 2.5 is assumed to use a typical sector antenna, such as that presented in [53].

MSs mobility can be modeled according to a random-walk (Markovian) mobil-

ity pattern [16, 17]. Only pedestrian mobility is considered in this chapter, which

assumes an average speed of 3 km/h. The current MS speed and direction of move-

ment are uniformly distributed within [1 km/h, 5 km/h] and [0,2], respectively.

These are held until the MS walks a distance of 5 m, after which new speed and di-rection are sorted. Other mobility models, including that of vehicular mobility, can

be found in [16, 17].

Because MSs move over the grid shown in Fig. 2.5 and because only a limi-

ted area is covered, MSs could eventually leave the coverage area. Additionally,

due to the geographic distribution of the co-channel sectors in Fig. 2.5, sites on

the border of the grid perceive less interference than those in middle of the grid,

which is termed a border effect. In order to allow infinite mobility over a limited

region and to avoid border effects, which are undesired, a wrap-around technique

is usually considered. Wrap-around techniques are usually based on cell replicationor on a geometric model which yields homogeneous average interference levels in

the whole grid. Herein, the wrap-around technique described in [59] is employed,

which consists of bending the grid in order to form a torus surface, as illustrated in

Fig. 2.6. Note that the described grid and mobility models can be directly applied to

other wireless networks.

2.4.2 Propagation Models

As it has been discussed in Chapter 1, radio communication is affected by large-

and small-scale fading, which ultimately result from reflection, refraction, and

diffraction of the transmitted radio waves [44, 56]. These effects are assumed in the


17/45


(a) Torus model (b) Torus mapping

Fig. 2.6 Infinite mobility model.

GSM/EDGE simulations considered in this chapter and their models are described

in the sequel.

There are different average path loss models, which are adequate for different

propagation scenarios [44, 56]. Herein, the OkumuraHata model is employed,which applies to urban and suburban environments where the average building

height is approximately uniform [53]. Considering this model and denoting by d

the distance in km between a BS and an MS, by fc the system central carrier fre-

quency in MHz, and by hBS the BS height in meters and measured with respect to

the average rooftop of buildings, the average path loss Lpl is given by

Lpl = 40(1 hBS 4 103) log(d) 18log(hBS) + 21log(fc) + 80 in dB. (2.2)

As the MS moves in the coverage area, large obstacles such as buildings mayobstruct the propagation path between the BS and MS causing fluctuations on the

received signal power, i.e., shadowing the received signal. Shadowing is usually

modeled by a lognormal random variable with standard deviation sf [44, 56]. Be-cause shadowing relates with the position of large obstacles in the coverage area,

it is position-dependent and spatially correlated. The modeling of the spatially cor-

related shadowing can be accomplished by sorting independent lognormal shadow

samples for a rectangular grid composed of points uniformly separated by a shad-

owing decorrelation distance dsf [59]. This model is termed a shadowing map and

for each BS in the network such a shadowing map is created. Then, the shadow-ing Lsf(x,y) associated with an arbitrary position (x,y) in the grid can be obtainedthrough linear interpolation.

Besides the spatial correlation of shadowing captured by the above model, it

is also worth modeling the spatial correlation of the shadowing perceived in the

links between different BSs and the same MS. This kind of correlation occurs, for

example, when the MS moves within a tunnel. This inter-BS shadowing correlation

can be modeled with help of an additional shadowing map, which is associated with

all MSs. In order to obtain the shadowing sample L(b,m)sf (x,y) for the link between

a BS b and an MS m, the shadowing samples L(b)sf (x,y) and L

(m)sf (x,y), obtained

respectively from the BS and MSs shadowing maps, are combined as

L(b,m)sf (x,y) =

1 sfL(b)sf (x,y) +

sfL

(m)sf (x,y), (2.3)


18/45


where 0 sf 1 is a coefficient which controls the amount of shadowing correla-tion among BSs [59].

Multi-path fading leads to deep fluctuations in the received signal power. It has

been modeled in the link-level simulations, where time-correlated fast fading is ge-

nerated using the well-known Jakes model [30].

2.4.3 Link Quality Measurements

Considering the presented radio propagation models, the measures considered in the

system simulations to evaluate the communication links between BSs and MSs are

discussed in the following.

The power Pr received by the MS depends on the transmit power Pt used by theBS and on the particular state of the link between BS and MS. The received power

Pr of an MS can be expressed as

Pr = Pt + Gant Lpl Lsf in dBm, (2.4)

where the antenna gain Gant, the average path loss Lpl, and the shadow fading Lsf can

be obtained considering the relative position of the BS and MS. Additionally, extra

gains/losses, such as cabling losses at the BS or additional antenna gains at the MS,

can be easily added/subtracted in (2.4). For simplicity, such additional gains/losses

are not considered here.Co-channel sectors share the same frequencies and generate interference. A com-

mon measure of the link quality corresponds to its SINR. Assume that MS i is served

by the sector i and let Nci denote the number of interfering co-channel sectors. De-

noting by pri,j the power received by MS i from sector j, and by the average noisepower, the SINR i of MS i is given by

i =pri,i

Nci

j=1, j=i

pri,j +

. (2.5)

For interference-limited scenarios, the average noise power can be neglectedand the SINR in (2.5) reduces to the SIR.

In the link-level simulations, curves such as bit error rate (BER), block error

rate (BLER), or frame erasure rate (FER) as functions of the SINR (or SIR) are ob-

tained by averaging the link performance over a long period of time. In this way, the

mean characteristics of mechanisms like data interleaving, fast fading, and fast in-

terference variations are captured into the link-level results. In the system-level sim-

ulations, measures of the SINR (or SIR) can be easily obtained and can be mapped

afterward into BER, BLER, or FER values using an adequate link-level curve.

Considering voice services, the average SINR of the eight half-bursts composing

a voice frame is mapped to an FER value. Considering data services, the average

SINR of the four bursts composing a radio block is mapped to a BLER value. Based


19/45


on the FER, in the case of the voice service, or on the BLER, in the case of data

services, a random test is used to determine whether a transmission has been suc-

cessful.

For the simulations considered in this chapter, the RXQUAL, MEAN BEP, and

CV BEP values shortly described in Section 2.3.1 cannot be measured as describedin the standard because they depend directly on RBER. Instead of the standard mea-

surements, the average SINR s over one reporting period is considered as link qua-lity measurement. It is computed by averaging the mean SINR of each block within

the reporting period and it is sent back to the BS by the MS. This model is applied

here to both voice and data services and a reporting period equal to one SACCH

multiframe is considered by default.

2.4.4 Traffic Models

Voice and data services have very different traffic patterns, thus requiring elaborate

traffic models adequate to their peculiarities. They define the activity of the MSs

and how the traffic is generated.

Dynamic arrival and departure of MSs voice calls or data sessions are considered

in the system. The arrival process is modeled in the dynamic simulations through

Poisson processes with specific arrival rates for each traffic type. In both voice and

data traffic cases, the interval between consecutive arrivals of new MSs in the system

is modeled by a negative exponentially distributed random variable [43].

In the modeling of the voice service, two aspects are taken into account: the

duration of the call and the speech activity during a call. The first aspect is modeled

through an exponential distribution with a 120 s mean. The activity model, however,

depends on the use or not of discontinuous transmission (DTX). In the case in which

DTX is disabled, the BS continuously transmits speech frames, even during the

periods in which the speaker is silent, thus generating interference during the whole

call. In the case in which DTX is used as an interference reduction mechanism,

voice activity must be modeled.

A slow voice activity detector is considered, which fits well in to the global sys-

tem for mobile communication (GSM) voice service whose minimum talking/silent

periods are of one SACCH multiframe [9]. The adopted voice activity model con-

siders a two-state Markov chain for simulating the transition between active and

silent states [26].

The state transition probabilities from active-to-silent Pas and from silent-to-active Psa can be determined from the equations:

Pas = 1 exp(Trep/Ta) and Psa = 1 exp(Trep/Ts), (2.6)

respectively, where Trep represents the duration of a reporting period and Ta and Tscorrespond to the mean duration of the active and silent stages. This leads to a mean

voice activity ofTa/(Ta + Ts). Typically, a mean voice activity of 60% is considered[22]. For the GSM/EDGE network considered in this chapter, Trep corresponds by


20/45


default to the duration of an SACCH multiframe and during silent periods DTX

disables the transmission of voice frames, thus reducing interference in the system.

Note that by adapting the values ofTrep, Ta, and Ts the presented voice traffic model

can be easily employed in other wireless networks.

The data traffic model of the WWW interactive service is fairly different fromthat of speech, having a strong bursty traffic characteristic. The adopted WWW

model considers the arrivals of packets within a session, the times between packets,

and the packet lengths [32]. It is a simplified version of the WWW model presented

in [53], which additionally considers packet calls. In [53], there are random variables

modeling the number of packets per packet call and the inter-arrival time between

such packets. The traffic model of [32] concentrates packets belonging to a packet

call into a single large packet. Table 2.3 presents a summary of the parameters,

distributions, and values of the WWW traffic model.

Table 2.3 world wide web (WWW) traffic model.

Parameter Value

Sessions

Distribution of the number of packet calls per session Geometric

Mean number of packet calls per session 10

Packet calls

Distribution of the reading time between packet calls Truncated ParetoMean reading time between packet calls TP 10 sPareto distribution parameters TP, kTP, and mTP 1.4, 3.45, and 120 sNumber of packets per packet call 1

Packets

Distribution of packet sizes Lognormal

Mean packet size 4,100 bytes

Standard deviation of packet size 30,000 bytes

Maximum packet size 100,000 bytes

2.4.5 Evaluation Metrics

Key performance indicators in wireless networks are mainly related to quality and

capacity measures. Other measures, such as blocking rate and channel reallocation

rate, may also be relevant in certain situations. The analyses presented in this chapter

are focused on the relative performance of the different RRM techniques, rather thanon absolute figures. In this section, some evaluation metrics and requirements are

defined.

The voice quality is expressed in terms of the FER, which takes into account the

percentage of lost speech frames with regard to the total number of speech frames.


21/45


The data quality, on the other hand, is defined as the mean packet bit rate r during

the session of an MS.

The individual QoS criteria, which determine the satisfaction of each MS, are

defined taking as reference the values presented in [24]. The following services

are considered: enhanced full rate (EFR), multi-rate at 5.9 kbit/s with full rate(MR59FR), and World Wide Web (WWW). The QoS requirements are given by

Voice (EFR): FERreq 1%,Voice (MR59FR): FERreq 0.6%,Data (WWW): rreq 10 kbit/s.

(2.7)

The global QoS criteria of the system are defined as the percentual of satisfied

users of each service. For voice a 95% satisfaction is required and for data either

95% or 90%, depending on the scenario.

Besides the QoS requirements, which apply to interference-limited systems,there is also the blocking criterion for voice. The blocking limit tolerated by the

system is of 2%, where the blocking rate is defined as the relation between the num-

ber of blocked MSs and the number of births that occurred during the period for

collecting the statistics.

The system capacity Cis expressed in terms of spectral efficiency, i.e., the offered

load per cell normalized by the amount of utilized spectrum. The spectral efficiency

is expressed as C= rtot/(BtotNcell), where rtot indicates the total load offered to thesystem in bit/s, Btot represents the frequency bandwidth available for traffic, and

Ncell corresponds to the number of cells.In the case of the voice service, the offered load is measured in Erlangs, while for

the data service it is measured in terms of the transmission rate (bit/s). The spectral

efficiency units are therefore measured in Erl/MHz/cell and bit/s/Hz/cell, for the

voice and data services, respectively.

The network capacity limit Cmax corresponds to the maximum load that can be

offered to the system before the QoS or blocking requirements are violated. In some

situations the capacity is also expressed in its normalized form, which consists of

dividing the capacity value by the maximum service capacity, i.e., Cnorm = C/Cmax.

Although the required QoS values introduced in this section are meant for aGSM/EDGE network, the same framework can be applied to other wireless net-

works with different QoS requirements.

2.5 RRM Performance in GSM/EDGE

In this section, the performance of different RRM techniques is evaluated in a

GSM/EDGE network by means of dynamic system level simulations employing the

models described in the previous section. It should be noted that the performance as-

sessment through simulations involves simplifications, which are required in order

to make this task mathematically and computationally tractable.


22/45


Due to such simplifications, the absolute performance results obtained through

the modeling and simulation of wireless networks might differ from those observed

in real systems. However, the relative analyses conducted by means of simulative

studies are valid since all the model parameters are kept consistent across the dif-

ferent scenarios. Moreover, a significant part of the performance gains observedin simulation studies can be usually obtained in real systems. In this way, impor-

tant insight on the performance of RRM strategies applied to wireless networks is

obtained, which is of crucial strategic importance in decision-making processes in-

volved in the design and operation of these systems.

Therefore, in this section it is important to focus on the relative performance

gains obtained by the RRM techniques compared to reference scenarios. It is

also worth mentioning that there exists extensive literature on RRM applied to

GSM/EDGE and other wireless networks. It is not our intent to be exhaustive in

covering the existing literature, so that we restrict ourselves to providing only keyreferences concerning each topic.

2.5.1 Overall scenario

In this chapter, a GSM/EDGE network covering an urban macrocellular scenario is

considered. Because frequency spectrum is a limited and expensive resource, tighter

frequency reuse patterns like 1/3 and 1/1 are usually pursued in the GSM/EDGE

networks. Tight frequency reuses allow for obtaining higher spectral efficiencies.They lead, however, to additional co-channel interference that must be efficiently

handled by means of RRM techniques. To investigate the performance of voice and

data services, simulations considering different RRM techniques have been done for

cellular grids implementing 1/3 and 1/1 frequency reuses and the obtained spectral

efficiency values have been compared with those obtained in reference scenarios.

The most relevant parameter values are shortly summarized in Table 2.4.

A central carrier frequency fc = 2,000 MHz and a BS height of 15 m above therooftop of buildings are considered. A system bandwidth of 2.4 MHz is taken into

account, which corresponds to 12 GSM carriers. RFH will be often employed and ineach simulation a total number of 10,000 calls or sessions are simulated. In most of

the considered cases, an interference-limited system is considered, i.e., noise power

is negligible and SINR values are equivalent to SIR values.

In the system, a shadowing standard deviation sf value of 6 dB, a shadowingdecorrelation distance dsf of 110 m, and an inter-BS shadowing correlation factor

sf of 0.4 are considered [58].

2.5.2 Power Control

In this section, the performance of some of the PC algorithms discussed in Chapter 1

is evaluated in the downlink of a GSM/EDGE network. The voice service


23/45


Table 2.4 Simulation parameters.

Parameter Value

System

Cell type Tri-sectored

Frequency reuse pattern 1/3 or 1/1

Frequency of operation 2,000 MHz

System bandwidth 2.4 MHz (12 GSM carriers of 200 kHz)

# of transceivers per sector 4 in frequency reuse 1/3, 12 in the frequency reuse 1/1

# of timeslots per carrier 8

Frequency hopping Random or cyclic

Transmission direction Downlink

Maximum transmit power 35 dBm

MS mobility Random-walk pedestrian model (3 km/h avg. speed)

# of simulated calls or sessions 10,000

Satisfaction degree 95% of voice calls, 90 or 95% of WWW sessions

Propagation effects

Average path loss 128.15 + 37.60log(d) in dB, cf. (2.2)Shadowing standard deviation 6 dB

Shadowing decorrelation distance 110 m

Inter-BS shadowing correlation factor 0.4

Fast fading Considered at the link-level

Services

Average voice call duration 120 sVoice codec EFR, MR59FR

Voice call average FER 1% with EFR, 0.6% with MR59FR

Voice blocking limit 2%

Discontinuous transmission Enabled, disabled

Mean active voice period 1.1 s

Mean silent voice period 0.7 s

Mean voice activity 60%WWW traffic Modeled according to [32], cf. Section 2.4

Link adaptation Enabled in ideal or non-ideal mode

Average session throughput 10 kbit/s

performance analysis considers two voice codecs, namely the EFR and the MR59FR.

WWW traffic is considered as the data service. The system performance with and

without PC is investigated for these services. In this section, a fraction of 95% of

the MSs should be satisfied either for the voice or the WWW service.

The following PC algorithms are considered in this section: the one proposed in

[21], which is termed here autonomous SINR balancing power control (ASBPC);

the one proposed in [13, 57], which is termed here soft dropping power control

(SDPC); and the up-down power control (UDPC) algorithm. All these algorithms

have been previously discussed in Chapter 1.

In this section, the reported s is represented by for simplicity of notation. Thethree considered PC algorithms are closed-loop algorithms based on the reported


24/45


SINR values. The algorithms share a similar mathematical structure, given by

P(k+1) = P(k) cPC(k) in dBm, (2.8)

where P(k)

represents the transmit power at iteration k, cPC is a PC feedback con-stant, and

(k) is the feedback error signal of PC [28].

For all algorithms, the time delay compensation (TDC) technique discussed in

Chapter 1 is employed to avoid instabilities due to measurement and report de-

lays [28]. Considering the delay of one period associated with reporting the average

SINR back to the BS, (2.8) becomes

P(k+1) = P(k) cPC(k1) in dBm. (2.9)

As a consequence, the power adjustment at iteration k+ 1 is performed based onoutdated information, since the effect of the power adjustment associated with iter-ation k is captured only in the SINR made available at the BS at iteration k+ 2. Inorder to avoid this, the effect of the power adjustment done at iteration kis predicted

and introduced in the feedback error signal under the assumption that the referred

PC command was successful. Thus, considering TDC, the PC iteration becomes

P(k+1) = P(k) cPCk + P(k) P(k1) in dBm. (2.10)

Considering TDC, the iterative formulation of the ASBPC, SDPC, and UDPC

algorithms of Chapter 1 can be written as

p(k+1) = p(k)

1 F +F tp

(k1)

(k)p(k)

in W, (2.11)

P(k+1) = P(k) SD(k) + P(k) P(k1) t(P(k))

in dBm, and (2.12)

P(k+1) = P(k) UD sign((k) + P(k) P(k1) t) in dBm, (2.13)

respectively.

For the ASBPC and SDPC algorithms, (k) of (2.8) and (2.10) relates to the

difference between the measured SINR and the target SINR t, while the constantcPC relates to the amount of the difference between measured and target SINRs that

is compensated at each PC iteration. For the UDPC, (k) relates to the sign of the

difference between the measured and target SINRs, which is limited to either 1,0, or +1, and the constant cPC relates to the power step UD introducing a fixedcompensation of the feedback error signal. For more details on the formulation of

power control iterations as control systems with feedback error signals refer to [28].

In particular for the voice service, several target SINRt

values for the PC algo-

rithms have been tested and the value resulting in the best spectral efficiency figures

has been selected. For UDPC and ASBPC, t values 2 dB lower or higher than theselected ones resulted in worse system performance. The target SINR values con-

sidered in this section are higher than the 8 and 4 dB required with the EFR and


25/45


MR59FR codecs to achieve average FERs of 1 and 0.6%, respectively. Such differ-

ences are due to the particular models described in Section 2.4. In fact, an outer loop

PC would be required in order to determine the most adequate target SINR for each

scenario [29]. However, such an outer loop PC is not considered here.

In the SDPC case, a fixed target SINR is not specified, but a value for the pa-rameter SD controlling the relationship between demanded power and target SINR.For the SDPC, SD value has been found experimentally, as suggested in [13], withSD = 0.6 providing the best results.

For the data service, a target SINR of 35 dB has been considered in order to

avoid the concurrence between PC and LA. In order to investigate the impact of the

actuation frequency of PC, different reporting periods have also been considered.

Such parameter settings will be discussed in more detail together with the results

obtained in the corresponding cases. The most relevant power control parameters

are listed in Table 2.5.

Table 2.5 Power control parameters.

Parameter Value

PC algorithms ASBPC, SDPC, and UDPC

Maximum transmit power 35 dBm

Minimum transmit power 5 dBm

Power levels Discrete in steps UD of 2 dBPower control time-step 1 iteration at each 480, 120, or 20 ms

Time delay compensation (TDC) EnabledF for ASBPC 1 (fastest convergence) [21, 28]SD for SDPC 0.6 (defined experimentally, as in [13])Minimum target SINR t,min for SDPC 8 dB for EFR, 4 dB for MR59FR, 6 dB for WWW

In this section, scenarios implementing tight frequency reuses of 1/3 and 1/1 are

considered and PC is employed to manage the co-channel interference and improve

the system performance. RFH and pedestrian mobility, cf. Table 2.4, are considered.

2.5.2.1 Power Control Performance for the Voice Service

Initially, the performance of PC for the voice service considering the EFR codec is

investigated. In order to determine the system capacity, simulations with increasing

offered loads have been conducted until reaching the QoS limits given in Table 2.4.

Figure 2.7 presents the percentual of satisfied MSs as a function of the voice spectral

efficiency in Erl/MHz/cell considering the EFR codec and the 1/3 frequency reuse.

In the cases in which PC is applied, DTX is also enabled.In Fig. 2.7, the capacity limits by interference and by blocking are also shown.

The system capacity is limited by interference whenever the blocking rate is below

2% but the fraction of satisfied MSs is lower than 95%, which corresponds to having

more than 5% of the MSs perceiving average FER higher than the values specified


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0 5 10 15 20 25 3094

95

96

97

98

99

100

Voice spectral efficiency in Erl/MHz/cell

FractionofsatisfiedMSsin%

Interference limit

Blocking

limit

35% 80%

160%

No DTX

DTX

UDPC

ASBPC

SDPC

Fig. 2.7 PC performance with EFR voice codec and reuse 1/3 (t = 16 dB for UDPC and ASBPC).

in Table 2.4. Oppositely, the system capacity is limited by blocking if more than

2% of the arriving calls are blocked due to unavailability of free channels while

the ongoing calls perceive acceptable average FER values. Given the number of

channels per sector and the target blocking rate, the blocking limit of the system

can be easily calculated using standard traffic engineering methods [44, 56]. Thecapacity limits by blocking and interference are drawn for all figures in this section,

but are labeled only in Fig. 2.7.

In Fig. 2.7, it can be seen that the system capacity without PC and DTX is consid-

erably lower than when these two features are enabled. Indeed, by enabling DTX a

considerable amount of unnecessary interference is eliminated from the system and

a capacity gain of more than 35% is obtained. By using ASBPC, a voice spectral ef-

ficiency of more than 23.5 Erl/MHz/cell is obtained, which represents an additional

gain superior to 80% compared to the case with DTX only. Compared to the case in

which both PC and DTX are disabled, the obtained spectral efficiency is about 1.5times higher, which shows that ASBPC can substantially improve the system capac-

ity. Such a high-capacity improvement comes from the reduction of the co-channel

interference performed by the PC algorithms, which employ only as much power as

required to attain the target QoS levels.

In spite of being able to perform larger power adjustments, the ASBPC provided

lower spectral efficiency figures than the UDPC. This result might be explained by

the selection ofF = 1, which according to [21, 28] results in the fastest conver-gence. However, this value ofF is not necessarily optimal and might lead to insta-

bility of the ASBPC algorithm, as discussed in [28]. Thus, it is possible that lowervalues ofF could lead to better spectral efficiencies. Anyway, the optimization ofthe parameter F has not been performed for the ASBPC algorithm. In this case, theUDPC works in a more stable fashion leading to higher spectral efficiency values.


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Indeed, it performs as good as the SDPC algorithm, which is allowed to make larger

power adjustments like the ASBPC but used a more conservative value for SD andinvolves a self-regulation of the target SINR [13, 28, 57]. The UDPC and SDPC al-

gorithms outperform here the ASBPC algorithm providing spectral efficiency gains

higher than 160% compared to the case without PC and DTX. As it can be seen inFig. 2.7, the UDPC and SDPC algorithms reach the blocking limit of 2% while there

are more than 95% of satisfied MSs in the system, i.e., in the considered scenario

PC turns the system into a blocking limited system.

Because the system capacity became limited by blocking, voice calls perceive

QoS levels higher than the target ones. This suggests tightening the frequency reuse

in order to make more channels available per sector and to potentially support higher

number of voice calls. The frequency reuse tightening increases the number of avail-

able channels per sector, but it incurs in a considerable increase of co-channel inter-

ference due to the smaller reuse distance. In Fig. 2.8, the fraction of satisfied MSsagainst the voice spectral efficiency is shown for a GSM/EDGE network implement-

ing a 1/1 frequency reuse and considering the EFR codec.

0 5 10 15 20 25 3094

95

96

97

98

99

100


FractionofsatisfiedMSsin%

No DTX

DTX

UDPC

ASBPC

SDPC

Fig. 2.8 PC performance with EFR voice codec and reuse 1/1 (t = 18 dB for UDPC and ASBPC).

It can be seen in Fig. 2.8 that the frequency reuse tightening results in a too

large increase of the average co-channel interference and causes an overall reduction

of the system spectral efficiency varying between 15 and 25%. The system might

support higher voice loads if more robust voice codecs, such as the MR59FR, are

used by the MSs. Such a codec allows the system to operate with acceptable voice

quality at considerably lower SINR levels.

In the following, the spectral efficiency of the system considering the MR59FR

codec is evaluated.


28/45


Figure 2.9(a) shows the fraction of satisfied MSs against the achieved voice spec-

tral efficiency considering the MR59FR codec and a 1/3 frequency reuse. In this fig-

ure, it can be seen that considerably higher voice loads are supported in the system

and, consequently, higher spectral efficiency values are achieved. Moreover, it can

be seen that the use of DTX without PC is already sufficient to bring the systemto a capacity limitation by blocking. Compared to the case without DTX and PC,

the obtained spectral efficiency gains are of about 25%. Using PC in this scenario

would only improve the QoS levels perceived by the MSs resulting in average FER

per call considerably lower than the target values given in Table 2.4. Such a case

is shown in Fig. 2.9(a) for the UDPC algorithm only, which provides a satisfaction

level much higher than that achieved with DTX and no PC with more than 99% of

satisfied MSs. In this case, a tightening of the frequency reuse might also be advis-

able. The results, analog to Fig. 2.8 but considering the MR59FR codec, are shown

in Fig. 2.9(b).

0 5 10 15 20 25 30 3594

95

96

97

98

99

100


Fractionof

satisfiedMSsin%

No DTX

DTX

UDPC

(a) Frequency reuse 1/3

0 5 10 15 20 25 30 3594

95

96

97

98

99

100


Fractionof

satisfiedMSsin%

No DTX

DTX

UDPC

ASBPC

SDPC

(b) Frequency reuse 1/1

Fig. 2.9 PC performance with MR59FR codec.

Differently from Fig. 2.8, where a tightening of the frequency reuse resulted in

too much interference to be managed, in Fig. 2.9(b) the use of a more robust codec

allows a larger amount of interference to be supported. In Fig. 2.9(b) the spectral

efficiency value achieved with the ASBPC at the capacity limit is almost the same

as that achieved in the 1/3 frequency reuse with the EFR codec, in spite of the

additional interference of the 1/1 frequency reuse. The same is valid for the UDPC,

which achieves about 25 Erl/MHz/cell as in Fig. 2.7. In Fig. 2.9(b), it can be seen

that SDPC outperforms the UDPC algorithm and achieves a spectral efficiency value

of about 28.5 Erl/MHz/cell. Thus, a spectral efficiency gain of about 15% is obtained

with acceptable QoS for voice calls. Moreover, no blocking has been verified since

a larger number of channels are available in the 1/1 scenarios.


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Additional capacity gains can be obtained by increasing the actuation frequency

of the PC. In order to do this, alternative schemes have been proposed in [2, 52],

which suggest to modify the PC signaling mechanisms in order to improve PC per-

formance. The first approach consists of modifying the SACCH channel structure

so that PC commands and link quality measurements could be transmitted in eachSACCH burst, i.e., at each 120 ms. This corresponds to multiplying the rate of the

power adjustments by 4. A second approach is to transmit PC commands and link

quality measurements in-band, resulting in a 24-fold improvement in the PC ac-

tuation frequency. Nevertheless, it is worth mentioning that both strategies might

reduce the protection of the control information sent over the SACCH and TCH

channels, since some more bits would be allocated for PC purposes [52]. Even con-

sidering such improvements, the PC actuation frequency in the GSM/EDGE net-

work is still too low to compensate for fast fading, especially for fast moving MSs.

As a comparison, the highest PC actuation frequency, i.e., 50 Hz considering oneiteration at each 20 ms, is still 30 times lower than that considered in WCDMA

systems, which corresponds to 1.5 kHz. According to [29], even when operating

at such frequency, wideband code division multiple-access (WCDMA) fast PC is

not able to perfectly compensate for fast fading of MSs with speeds superior to

50 km/h.

Table 2.6 shows the performance of the SDPC algorithm considering different re-

porting periods, i.e., different actuation frequencies for the PC. SDPC is considered

because it performed as good as or outperformed the ASBPC and UDPC algorithms.

A frequency reuse of 1/3 is used with the EFR service because better spectral effi-

ciency values have been achieved in this case. For the same reason, the 1/1 frequency

reuse is selected when considering the MR59FR codec.

Table 2.6 Performance of the

SDPC algorithm with reduced

reporting periods (higher

actuation frequency).

EFR, 1/3 MR59FR, 1/1

Reporting period in ms 480 120 20 480 120 20

Capacity in Erl/MHz 25 25 25 28.5 35 31.3

% of satisfied MSs 96 98 98 95 95 95

In Table 2.6, it can be seen that either QoS (with EFR) or capacity gains (with

MR59FR) can be obtained by increasing the actuation frequency of the PC. For

the MR59FR, a spectral efficiency gain of about 20% is achieved by perform-

ing PC adjustments at each 120 ms instead of at each 480 ms. However, adjust-

ing transmit powers at each 20 ms does not enhance spectral efficiency. The rea-

son for the latter result is that an increase of the actuation frequency leads to a

higher variance of the interference in the system, which is harder to be tracked

and compensated by the PC. Comparing the 35 Erl/MHz/cell supported in the 1/1frequency reuse with MR59FR and power adjustments at each 120 ms, a spectral

efficiency gain superior to 170% is obtained compared to the scenario without PC

and DTX.


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2.5.2.2 Power Control Performance for the WWW Service

In the GSM/EDGE network, the LA works on the same time basis as PC, i.e., with

one adjustment at each 480 ms. Such adjustments can be made based on the reported

average MEAN BEP and average CV BEP measured during one SACCH frame.Similarly to the voice service, a delay of one reporting period is involved before

measurements become available at the BS.

If the target SINR of the PC algorithm is set outside of the range of SINR values

covered by the LA, the performance of PC is strongly limited and no considerable

capacity gain is obtained through PC, as it has been shown in [37]. This occurs

mainly because PC will only apply for a few connections, whose link quality is

really high, for instance, with SINR values above 35 dB. In [ 37], it has also been

shown that an increase in the actuation frequency of LA leads to capacity gains of

about 20%, which are similar to those previously shown for the voice service whenincreasing the actuation frequency of PC. Anyway, PC control does not provide any

considerable capacity gain in this case too.

PC can provide capacity gains to the WWW service if the dynamic power range

is extended from 10 to 30 dB and if the target SINR of the PC algorithm is allowed

to lie inside the range covered by the LA, i.e., if the prioritization of LA over PC

and the minimum transmit power constraint for the reliable reception of the USF are

disabled.

In order to show this, the SDPC algorithm is considered in the sequel for the

WWW service. A minimum target SINR of 6 dB has been considered, which cor-

responds to the QoS requirement of 10 kbit/s according to the link-level curves

in Fig. 2.3. As before, the value 0.6 has been used for the parameter SD. In or-der to reduce the impact of the scheduling algorithm over the PC, the well-known

first-in-first-served (FIFS) scheduling discipline is used, which maintains a chan-

nel allocated to the same MS for the total time needed to transmit its current

packet. Considering these assumptions, Fig. 2.10 shows the capacity and QoS gains

achieved with PC for the WWW service.

In Fig. 2.10(a), the fraction of satisfied MSs against the data spectral efficiency in

bit/s/Hz/cell is shown. As it can be noted, PC can provide spectral efficiency gains

of about 30% if a 10 dB dynamic power range is assumed and the prioritization

of LA over PC is ignored. Additional gains of 15% are obtained by increasing the

dynamic power range from 10 to 30 dB.

Figure 2.10(b) shows the 10th percentile of the MSs average packet throughput,

denoted by r10%, against the data spectral efficiency. In this figure, it can be seen

that for low offered loads the average throughput achieved by 90% of the MSs is

higher when PC is not used. Oppositely, for higher loads the average throughput

perceived by 90% of the MSs is higher than when PC is disabled. This effect is

more accentuated for a 30 dB dynamic power range. Consequently, the curves for

the average throughput of 90% of the MSs with and without PC cross each other.The reasons for this crossing are as follows.


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0 0.1 0.2 0.3 0.4 0.5 0.694

95

96

97

98

99

100

Data spectral efficiency in bit/s/Hz/cell

No Power ControlSDPC, 10 dB dyn. rangeSDPC, 30 dB dyn. range

FractionofsatisfiedM

Ssin%

(a) Fraction of satisfied MSs

0 0.1 0.2 0.3 0.4 0.5 0.69

10

11

12

13

14

Data spectral efficiency in bit/s/Hz/cell

No Power ControlSDPC, 10 dB dyn. rangeSDPC, 30 dB dyn. range

r10%

inkbit/s

(b) Average packet throughput

Fig. 2.10 PC performance for the WWW service in frequency reuse 1/3.

For lower loads, a smaller number of data sessions are present in the system

and LA maximizes the throughput

optimization2

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